Whether they like it or not, every company today is in a race. The finish line: to become a fully-fledged 21st-century data-driven business.
Some companies have zoomed ahead, their proverbial engines turbocharged by data-powered processes. They are the Ubers, the JP Morgan Chases, the Pubmatics of this world.
Others are jockeying for position in the middle of the pack, upgrading to modern data stacks and fueling their operations with data. Still other companies are stuck at the starting line.
The good news is the race is far from over. No one has waved the checkered flag. The bad news is that too many of the companies trying to build the perfect data-driven race car have focused on building the best engine and forgotten about an equally important part — the instrument panel.
To drive fast and safely, every car needs a digital dashboard outfitted with modern gauges and screens, fed by cameras and sensors that warn you of potential dangers, including cars on the freeway lurking in your blind spots. A modern Formula One race car shouldn’t rely on the crude dials and warning lights of a 1920s roadster, after all.
Yet, that is what companies are doing by fixating on what’s under the hood but ignoring what’s behind the wheel. They are investing in massive digital transformations to inject data and analytics into speeding up their operations. However, they are underinvesting in data observability tools that would let them understand how effectively their newly data-driven processes are performing, or what is causing bottlenecks and bad data and other problems.
In other words, they are suffering from huge data blind spots. Or, put another way, their data is stuffed into a black box. Whatever the analogy, these companies can’t see what’s happening with their data. And they are quickly falling behind competitors as a result.
Business Observability Requires Data Observability
Here’s an example of what can go wrong if you have data blind spots. Imagine a product manufacturer that has digitized its entire supply chain, unifying multiple enterprise applications and databases, building streaming data pipelines, and creating real-time analytics applications. For the manufacturer to remain competitive, gaining strong, ongoing visibility over its inventory, orders, and the status of its channel partners, was mission critical. Some call this real-time state of awareness business observability, while others call it operational intelligence.
As diligent as the company was about gaining business observability, it completely ignored data observability. So it had no visibility into what was going on with its data pipelines, repositories, and applications. So when an executive questioned a report with strange-sounding numbers, the data engineer had no easy way to check whether the source data was reliable or not.
Or when a data scientist was browsing for datasets to use in a machine learning application, they had no way to differentiate between trustworthy and untrusted data.
Or when orders begin to pile up due to sluggish data pipelines connected to inventory and order systems, it took hours for data engineers to track down the root cause of the bottleneck, causing hundreds of thousands of dollars in delayed or lost sales.
In other words, as the company made data the heart of its real-world supply chain, it still had massive blind spots around its data supply chain. And since the company’s upgraded operations now depend heavily on data, this lack of visibility made its operations vulnerable to disruption at any time, without any warning.
A Real-World Example
Here’s another example from Thai communications provider, True Corporation. In many ways, True Corporation was far ahead to competitors in its data journey. The company oversaw a massive 8 petabyte Hadoop data lake spread across hundreds of servers. This data enabled True Corporation to measure and boost customer satisfaction, detect customer fraud, and also analyze customer behavior for product recommendations tailored to each customer. These were boosting True Coporation's revenue. Naturally, the company wanted to expand this.
So True Corporation kept adding data sources, faster data clusters and bigger storage. Despite the more powerful hardware, serious bottlenecks emerged, so severe that more than half of its ingested data remained unprocessed. Worse, True Corporation had no idea what was the cause: hardware bugs, misconfigured resource managers, bad code — or all of the above.
Without multidimensional data observability, True Corporation was left stymied, unable to deploy new data-driven business processes nor accelerate existing ones. What started off as a minor blind spot caused True Corporation to spin out onto the side of the road and crash.
Seven Questions to Ask
Are you or your peers still unconvinced that your organization suffers from data blind spots? Then ask yourself these seven questions, which are appropriate whether your role is a Chief Data Officer, VP of Engineering, Data Ops team lead, data engineer, or even COO or CFO.
- Do you have complete visibility of your data supply chain?
- Are you blind to the context of why your data issues are erupting?
- Are you able to get a good answer if you ask, ‘Can I trust this dataset?’
- Are you the first to know and the first to fix a data problem?
- Do you know if your databases and data warehouses, whether Hadoop, Databricks, or Amazon EMR, are running at peak efficiency?
- What will the bill for Snowflake or other cloud database provider look like next month?
- When do you find out if your data-related SLOs and SLAs are failing?
Eliminate Your Data Blind Spots
Getting internal agreement that data blind spots is a problem is the hard part. Eliminating data blind spots, by comparison, is simple: deploy a multi-dimensional data observability solution that provides real-time alerting and correlated analytical insights to help you keep data flowing and error-free wherever it is traveling, stored, or being processed.
Take the area of data performance. When your data powers mission-critical operations in real time, you can’t afford any data slowdowns, much less bottlenecks. Multi-dimensional data observability not only tracks the speed of your data pipelines, but analyzes the data using machine learning to forecast potential outages. With user-set rules, actions can be automatically taken to avert outages and ensure compliance with your SLAs.
Multi-dimensional data observability also helps companies in the area of data reliability and governance. More data, more sources, and more data transformations all create more problems with data, such as lack of trust, redundancy, poor searchability, and outright errors. Multi-dimensional data observability platforms offer tools such as data catalogs, data quality, data lineage, and data discovery that minimize the creation of bad data, make data easily tracked through its lifecycle, and prevent money-bleeding data silos and dark data pools.
Because of the cloud's easy scalability, the costs of data processing and storage can quickly spiral out of control, especially now that IT no longer rules the roost. A multi-dimensional data observability platform can provide the cost of each data repository and pipeline in real time, as well as ML-trained cost projections and cost policy tools that establish bumper guards to allow businesses to scale safely. For firms serious about value engineering and cloud FinOps, data observability is a must-have.
How Data Observability Helped True Digital
Let’s go back to the company that was suffering from data bottlenecks, True Digital. After the company deployed a data observability platform from Acceldata, it quickly pinpointed which data pipelines were getting clogged up, and why. In particular, Acceldata Pulse, our data performance solution, provided True Digital’s engineers with the right configuration settings and schedule to optimize its data speeds and prevent slowdowns. Pulse also provided accurate real-time alerts for True Digital’s many streaming data feeds that minimized false alarms.
Armed with Pulse, True Digital was able to improve its data speeds and reduce unplanned outages, enabling its analytics teams to support more lines of business. At the same time, True Digital was able to slash its bandwidth and storage needs, the latter by almost 2 petabytes, or 25 percent. It also reduced annual software licensing costs by 25 percent and system processing costs by $1 million a year.
Not All Observability Is Created Equal
Some so-called data observability systems only provide visibility into a single aspect of your data operations, such as data reliability or data performance or data costs. Others can only provide visibility into cloud environments, but not on-premises ones, or vice-versa. Still others can only peer into specific platforms.
As for Application Performance Management (APM) tools, they only provide observability into the application layer, not the data or infrastructure layers. That means APM cannot validate the quality of data pipelines. Nor can APM analyze complete datasets for quality and reliability, or help you avoid skewed data. Nor can it correlate root causes in the data layer to enable your data engineers to fix data bottlenecks or data errors quickly.
Even if your company has an APM solution, you’ll still be lacking visibility and correlation capabilities for your business data at every layer. The only way to achieve this is with a unified single pane of glass over your data — which is what Acceldata provides. Contact us to get a demo today.
Photo by Taras Chernus on Unsplash